Method, device, and program for determining risk of atheroma

Abstract

Provided are a method, a device, and a program for accurately determining the risk of an atheroma. This method for determining the risk of an atheroma includes a condition determination step and a risk determination step. In the condition determination step, it is determined whether or not an atheroma shown in one or more tomographic images of a blood vessel of a patient which have been acquired by an imaging method utilizing optical interference satisfies the following conditions (a) and (b): (a) the atheroma has a fibrous cap having a thickness that is not more than a predetermined threshold value; and (b) the atheroma contains cholesterol crystals. In the risk determination step, when it has been determined that the atheroma satisfies the conditions (a) and (b), it is determined that the atheroma satisfying the conditions (a) and (b) has higher risk compared with an atheroma that satisfies one or neither of the conditions (a) and (b).

Description

Method, apparatus, and program for determining the risk of atherosclerotic plaque

[0001] The present invention relates to a method, an apparatus, and a program for determining the risk of atherosclerotic plaque.

[0002] Among atherosclerotic plaques formed in the coronary artery, thin-cap fibroatheroma (TCFA), which has a thin cap, is known to be an unstable atherosclerotic plaque with a high risk of causing acute myocardial infarction in the future. In this regard, optical coherence tomography has been used as one method for evaluating atherosclerotic plaques in vivo (Non-Patent Documents 1 to 4).

[0003] F Otsuka et al., Nature Reviews Cardiology, Vol.11, 379-389 (2014)K. Fujii et al., JACC Cardiovascular Imaging. Vol.8, No.4, 451-460 (2015)A. J. Brown et al., EuroIntervention 2015; 11: e1A. J. Brown et al., Circ Cardiovasc Imaging. 2015; 8(10): e003487

[0004] However, conventionally, it has been difficult to accurately determine the risk of atherosclerotic plaque in tomographic images of the coronary artery obtained by optical coherence tomography.

[0005] The present invention has been made in view of the above problems, and one of its objects is to provide a method, an apparatus, and a program for accurately determining the risk of atherosclerotic plaque.

[0006] [1] A method for determining the risk of atherosclerosis according to one embodiment of the present invention for solving the above problems includes: a condition determination step of determining whether atherosclerosis visible in one or more tomographic images of a patient's blood vessels obtained by optical interference imaging satisfies the following conditions: (a) the atherosclerosis has a fibrous capsule with a thickness of less than or equal to a predetermined threshold; and (b) the atherosclerosis contains cholesterol crystals; and a risk determination step of determining that, if the atherosclerosis satisfies conditions (a) and (b) in the condition determination step, the atherosclerosis that satisfies conditions (a) and (b) is a higher-risk atherosclerosis than atherosclerosis that does not satisfy one or both of conditions (a) and (b). According to the present invention, a method for accurately determining the risk of atherosclerosis is provided.

[0007] [2] The method of [1] above may also be modified so that, if it is determined in the condition determination step that the atherosclerosis satisfies conditions (a) and (b), then in the risk determination step, it is determined that the atherosclerosis that satisfies conditions (a) and (b) is a theatherosclerosis that has a higher risk of causing vascular disease or complications during treatment compared to atherosclerosis that does not satisfy one or both of conditions (a) and (b).

[0008] [3] The method of [1] or [2] above may further include an additional condition determination step in which, if it is determined in the condition determination step that the atherosclerosis satisfies conditions (a) and (b), it is determined whether the atherosclerosis that satisfies conditions (a) and (b) also satisfies one or more additional conditions selected from the group consisting of: (c1) the representative value of the number of cholesterol crystals is greater than or equal to a predetermined threshold; (c2) the representative value of the number of layers of cholesterol crystals in the radial direction of the blood vessel is greater than or equal to a predetermined threshold; and (c3) the shortest distance between the cholesterol crystals and the inner surface of the blood vessel is less than or equal to a predetermined threshold; and if it is determined in the additional condition determination step that the atherosclerosis satisfies one or more of the additional conditions, it is determined that a specific atherosclerosis that satisfies conditions (a) and (b) and one or more of the additional conditions is a higher-risk atherosclerosis than an atherosclerosis that satisfies conditions (a) and (b) and does not satisfy the additional conditions that the specific atherosclerosis satisfies.

[0009] [4] Any of the methods described in [1] to [3] above may further include an additional condition determination step in which, if it is determined in the condition determination step that conditions (a) and (b) are met, an additional condition determination step in which it is determined whether the atherosclerotic plaque that meets conditions (a) and (b) also meets one or more additional conditions selected from the group consisting of: (d1) the representative value of the distribution angle of the atherosclerotic plaque in the cross-section of the blood vessel is greater than or equal to a predetermined threshold; and (d2) the length of the atherosclerotic plaque in the longitudinal direction of the blood vessel is greater than or equal to a predetermined threshold; and if it is determined in the additional condition determination step that the atherosclerotic plaque meets one or more of the additional conditions, an additional risk determination step in which it is determined that a specific atherosclerotic plaque that meets conditions (a) and (b) and also meets one or more of the additional conditions is a higher-risk atherosclerotic plaque than an atherosclerotic plaque that meets conditions (a) and (b) and does not meet the additional conditions that the specific atherosclerotic plaque meets.

[0010] [5] A risk determination device for atherosclerosis according to one embodiment of the present invention for solving the above problems includes: a condition determination unit that determines whether atherosclerosis visible in one or more tomographic images of a patient's blood vessels obtained by an imaging method using optical interference satisfies the following conditions: (a) the atherosclerosis has a fibrous capsule with a thickness of less than or equal to a predetermined threshold; and (b) the atherosclerosis contains cholesterol crystals; and a risk determination unit that, when the condition determination unit determines that the atherosclerosis satisfies conditions (a) and (b), generates determination result data indicating that the atherosclerosis satisfying conditions (a) and (b) is a higher-risk atherosclerosis than atherosclerosis that does not satisfy one or both of conditions (a) and (b). According to the present invention, a device for accurately determining the risk of atherosclerosis is provided.

[0011] [6] The apparatus of [5] may also be configured such that, when the condition determination unit determines that the atherosclerosis satisfies conditions (a) and (b), the risk determination unit generates determination result data indicating that the atherosclerosis that satisfies conditions (a) and (b) is at a higher risk of causing vascular disease or complications during treatment compared to atherosclerosis that does not satisfy one or both of conditions (a) and (b).

[0012] [7] The apparatus of [5] or [6], when the condition determination unit determines that the atherosclerosis satisfies conditions (a) and (b), the condition determination unit further determines that the atherosclerosis that satisfies conditions (a) and (b) is: (c1) the representative value of the number of cholesterol crystals is greater than or equal to a predetermined threshold; (c2) the representative value of the number of layers of cholesterol crystals in the radial direction of the blood vessel is greater than or equal to a predetermined threshold; and (c3) the shortest distance between the cholesterol crystals and the inner surface of the blood vessel is greater than or equal to a predetermined threshold The condition determination unit determines whether the atherosclerosis satisfies one or more additional conditions selected from the group consisting of "less than or equal to the value", and if the condition determination unit determines that the atherosclerosis satisfies one or more of the additional conditions, the risk determination unit may generate determination result data indicating that the specific atherosclerosis that satisfies conditions (a) and (b) and one or more of the additional conditions is a higher-risk atherosclerosis than atherosclerosis that satisfies conditions (a) and (b) and does not satisfy the additional conditions that the specific atherosclerosis satisfies.

[0013] [8] If the condition determination unit determines that the atherosclerosis satisfies conditions (a) and (b), the condition determination unit may determine whether the atherosclerosis, which has been determined to satisfy conditions (a) and (b), further satisfies one or more additional conditions selected from the group consisting of: (d1) the representative value of the distribution angle of the atherosclerosis in the cross-section of the blood vessel is greater than or equal to a predetermined threshold; and (d2) the length of the atherosclerosis in the longitudinal direction of the blood vessel is greater than or equal to a predetermined threshold; and if the condition determination unit determines that the atherosclerosis satisfies one or more of the additional conditions, the risk determination unit may generate determination result data indicating that the specific atherosclerosis, which satisfies conditions (a) and (b) and one or more of the additional conditions, is a higher-risk atherosclerosis than an atherosclerosis that satisfies conditions (a) and (b) and does not satisfy the additional conditions that the specific atherosclerosis satisfies.

[0014] [9] A risk assessment program for atherosclerosis according to one embodiment of the present invention for solving the above problems involves causing a computer to perform a condition determination step of determining whether atherosclerosis visible in one or more tomographic images of a patient's blood vessels acquired by an imaging method using optical interference satisfies the following conditions: (a) the atherosclerosis has a fibrous capsule whose thickness is less than or equal to a predetermined threshold; and (b) the atherosclerosis contains cholesterol crystals; and a risk assessment step of generating determination result data indicating that, if the condition determination step determines that the atherosclerosis satisfies conditions (a) and (b), the atherosclerosis that satisfies conditions (a) and (b) is a higher-risk atherosclerosis than atherosclerosis that does not satisfy one or both of conditions (a) and (b). According to the present invention, a program for accurately determining the risk of atherosclerosis is provided.

[0015]

[10] The program in [9] above may cause the computer to execute the risk determination step, which generates determination result data indicating that, if the program in the condition determination step determines that the atherosclerosis satisfies conditions (a) and (b), the atherosclerosis that satisfies conditions (a) and (b) is at a higher risk of causing vascular disease or complications during treatment compared to atherosclerosis that does not satisfy one or both of conditions (a) and (b).

[0016]

[11] The program of [9] or

[10] above determines that in the condition determination step the atherosclerosis satisfies conditions (a) and (b), and further determines that the atherosclerosis satisfying conditions (a) and (b) is from the group consisting of: (c1) the representative value of the number of cholesterol crystals is greater than or equal to a predetermined threshold; (c2) the representative value of the number of layers of cholesterol crystals in the radial direction of the blood vessel is greater than or equal to a predetermined threshold; and (c3) the shortest distance between the cholesterol crystals and the inner surface of the blood vessel is less than or equal to a predetermined threshold. The system may further perform an additional condition determination step, which determines whether or not one or more additional conditions are met, and an additional risk determination step, which generates determination result data indicating that a specific porridge that satisfies conditions (a) and (b) and one or more additional conditions is a higher-risk porridge than a porridge that satisfies conditions (a) and (b) and does not satisfy the additional conditions that the specific porridge satisfies.

[0017]

[12] Any of the programs in [9] to

[11] above may cause the computer to perform an additional condition determination step in which, if it is determined in the condition determination step that the atherosclerosis satisfies conditions (a) and (b), it determines whether the atherosclerosis that satisfies conditions (a) and (b) further satisfies one or more additional conditions selected from the group consisting of: (d1) the representative value of the distribution angle of the atherosclerosis in the cross-section of the blood vessel is greater than or equal to a predetermined threshold; and (d2) the length of the atherosclerosis in the longitudinal direction of the blood vessel is greater than or equal to a predetermined threshold; and an additional risk determination step in which, if it is determined in the additional condition determination step that the atherosclerosis satisfies one or more of the additional conditions, it generates determination result data indicating that a specific atherosclerosis that satisfies conditions (a) and (b) and one or more of the additional conditions is a higher-risk atherosclerosis than an atherosclerosis that satisfies conditions (a) and (b) and does not satisfy the additional conditions that the specific atherosclerosis satisfies.

[0018] According to the present invention, a method, apparatus, and program are provided for accurately determining the risk of atherosclerosis.

[0019] This is an explanatory diagram showing an example of the main hardware configuration of a device according to one embodiment of the present invention. This is a block diagram showing an example of the main functions realized by a device according to one embodiment of the present invention. This is a flowchart showing an example of steps performed in a method according to one embodiment of the present invention, performed by a device according to one embodiment of the present invention, or caused by a program according to one embodiment of the present invention to be executed by a computer. This is a flowchart showing another example of steps performed in a method according to one embodiment of the present invention, performed by a device according to one embodiment of the present invention, or caused by a program according to one embodiment of the present invention to be executed by a computer. This is a flowchart showing yet another example of steps performed in a method according to one embodiment of the present invention, performed by a device according to one embodiment of the present invention, or caused by a program according to one embodiment of the present invention to be executed by a computer. This is an explanatory diagram showing the calculation results of positive predictive value, negative predictive value, sensitivity and specificity in Example 1 according to one embodiment of the present invention. This is an explanatory diagram showing the evaluation results of the lipid content index of atherosclerosis in Example 2 according to one embodiment of the present invention. This is an explanatory diagram showing an example of a tomographic image used for determination in Example 3 according to one embodiment of the present invention. This is an explanatory diagram showing another example of a tomographic image used for determination in Example 3 according to one embodiment of the present invention. This is an explanatory diagram showing the evaluation results in Example 3 according to one embodiment of the present invention. This is an explanatory diagram showing an example of a tomographic image used for determination based on the number of cholesterol crystals in Example 4 according to one embodiment of the present invention. This is an explanatory diagram showing another example of a tomographic image used for determination based on the number of cholesterol crystals in Example 4 of one embodiment of the present invention. This is an explanatory diagram showing the evaluation results based on the number of cholesterol crystals in Example 4 of one embodiment of the present invention. This is an explanatory diagram showing an example of a tomographic image used for determination based on the number of layers of cholesterol crystals in Example 4 of one embodiment of the present invention. This is an explanatory diagram showing another example of a tomographic image used for determination based on the number of layers of cholesterol crystals in Example 4 of one embodiment of the present invention. This is an explanatory diagram showing the evaluation results based on the number of layers of cholesterol crystals in Example 4 of one embodiment of the present invention. This is an explanatory diagram showing an example of a tomographic image used for determination based on the shortest distance between the cholesterol crystal and the inner surface of the blood vessel in Example 4 of one embodiment of the present invention.This is an explanatory diagram showing another example of a tomographic image used for determination based on the shortest distance between cholesterol crystals and the inner surface of blood vessels in Example 4 of one embodiment of the present invention. This is an explanatory diagram showing the evaluation results based on the shortest distance between cholesterol crystals and the inner surface of blood vessels in Example 4 of one embodiment of the present invention. This is an explanatory diagram showing an example of a tomographic image used for determination based on the distribution angle of atherosclerosis in Example 4 of one embodiment of the present invention. This is an explanatory diagram showing another example of a tomographic image that was the subject of determination based on the distribution angle of atherosclerosis in Example 4 of one embodiment of the present invention. This is an explanatory diagram showing the evaluation results length of atherosclerosis in Example 4 of one embodiment of the present invention.

[0020] One embodiment of the present invention is described below. However, the present invention is not limited to this embodiment.

[0021] As one aspect, this embodiment includes a method for determining the risk of atherosclerosis (hereinafter referred to as "this method"), which includes a condition determination step of determining whether atherosclerosis visible in one or more tomographic images of a patient's blood vessels acquired by an imaging method utilizing optical interference satisfies the following conditions: (a) the atherosclerosis has a fibrous capsule whose thickness is less than or equal to a predetermined threshold; and (b) the atherosclerosis contains cholesterol crystals; and a risk determination step of determining, if the condition determination step determines that the atherosclerosis satisfies conditions (a) and (b), that atherosclerosis that satisfies conditions (a) and (b) is a higher-risk atherosclerosis than atherosclerosis that does not satisfy one or both of conditions (a) and (b).

[0022] In other words, the inventors of the present invention diligently studied technical means for accurately identifying high-risk atherosclerotic plaques based on tomographic images of blood vessels. As a result, they independently discovered that by using the condition that the atherosclerotic plaque contains cholesterol crystals, in addition to the condition that the thickness of the fibrous capsule of the atherosclerotic plaque is below a predetermined threshold, high-risk atherosclerotic plaques can be identified with higher accuracy than before, and thus completed the present invention.

[0023] In this method, the risk of atherosclerosis is determined by analyzing tomographic images of blood vessels. Therefore, this method is preferably performed using a computer with a program for analyzing tomographic images installed.

[0024] Therefore, in other respects, this embodiment includes a plaque risk determination device (hereinafter referred to as "this device") which includes: a condition determination unit that determines whether atherosclerotic plaques visible in one or more tomographic images of a patient's blood vessels acquired by an imaging method utilizing optical interference satisfy the following conditions: (a) the plaque has a fibrous capsule with a thickness of less than or equal to a predetermined threshold; and (b) the plaque contains cholesterol crystals; and a risk determination unit that, when the condition determination unit determines that the plaque satisfies conditions (a) and (b), generates determination result data indicating that the plaque satisfying conditions (a) and (b) is a higher-risk plaque compared to plaques that do not satisfy one or both of conditions (a) and (b).

[0025] Furthermore, this embodiment also includes, as another aspect, a plaque risk determination program (hereinafter referred to as "this program") which causes a computer to execute a condition determination step of determining whether atherosclerotic plaques visible in one or more tomographic images of a patient's blood vessels acquired by optical interference imaging satisfy the following conditions: (a) the plaque has a fibrous capsule whose thickness is below a predetermined threshold; and (b) the plaque contains cholesterol crystals; and a risk determination step of generating determination result data indicating that, if the condition determination step determines that the plaque satisfies conditions (a) and (b), the plaque that satisfies conditions (a) and (b) is a higher-risk plaque than plaque that does not satisfy one or both of conditions (a) and (b).

[0026] In other words, at least a portion of the steps included in this method is preferably performed using this device. Furthermore, this program causes a computer to perform at least a portion of the steps included in this method and / or at least a portion of the steps performed by this device. For this reason, this device preferably includes a computer on which this program is installed.

[0027] Therefore, the following description will mainly focus on an embodiment of this method, using the apparatus including a computer on which this program is installed, as an example of this embodiment. Note that this program may be a computer program, a computer program product, or a non-temporary tangible computer-readable recording medium, which includes instructions that cause the computer to execute at least some of the steps included in this method and / or at least some of the steps performed by the apparatus when the program is executed by the computer.

[0028] In this embodiment, the method for acquiring the tomographic image used for determination is not particularly limited as long as it is an imaging method utilizing optical interference. However, for example, an imaging method utilizing near-infrared light optical interference is preferably used, and more specifically, optical coherence tomography (OCT) or optical frequency domain imaging (OFDI) is preferably used.

[0029] In this embodiment, the patients to be evaluated are not particularly limited to patients diagnosed with the need to obtain tomographic images of their blood vessels, but are, for example, patients diagnosed with vascular disease. Examples of vascular diseases include coronary artery disease (e.g., acute myocardial infarction, angina pectoris, or other diseases resulting from occlusion or stenosis of the coronary arteries), peripheral artery disease (e.g., arteriosclerosis obliterans, acute arterial occlusion, or other diseases resulting from occlusion or stenosis of peripheral blood vessels), and cerebrovascular disease (carotid artery stenosis, intracranial artery stenosis, atherothrombotic cerebral infarction, or other diseases resulting from occlusion or stenosis of cerebral blood vessels).

[0030] Furthermore, the blood vessels subject to determination in this embodiment are not particularly limited as long as they are capable of forming atherosclerotic plaques, but are selected from the group consisting of, for example, coronary arteries (left coronary artery (left main trunk, left anterior descending branch, or left circumflex branch), or right coronary artery), peripheral blood vessels (for example, common femoral artery, superficial femoral artery, popliteal artery, anterior tibial artery, posterior tibial artery, peroneal artery, brachial artery, subclavian artery, or renal artery), and cerebral blood vessels (for example, common carotid artery, internal carotid artery, external carotid artery, middle cerebral artery, anterior cerebral artery, posterior cerebral artery, or vertebral artery).

[0031] Therefore, the following will mainly describe a method for determining the risk of atherosclerosis using one or more tomographic images (hereinafter referred to as "OCT images") of the coronary arteries of patients with coronary artery disease, obtained by an OCT device (hereinafter referred to as "OCT device").

[0032] Figure 1 shows an example of the main hardware configuration of the device 100. In the example shown in Figure 1, the device 100 includes a storage unit 110, a control unit 120, a display unit 130, an input unit 140, and a communication unit 150. The device 100 is not particularly limited as long as the effects of the present invention are obtained, but it is preferably a computer. That is, the device 100 includes, for example, a personal computer, a smartphone, a tablet terminal, or a wearable terminal.

[0033] The storage unit 110 is an element and / or device that stores programs and data necessary for the processing performed by the device 100. That is, the storage unit 110 includes, for example, volatile memory such as RAM (random access memory) and non-volatile memory such as hard disk or flash memory.

[0034] The control unit 120 is one or more processors that execute the processing of the device 100. The control unit 120 executes processing corresponding to the steps included in this method (for example, arithmetic processing and image generation processing) based on, for example, a program stored in the storage unit 110 and / or a program supplied via a network (not shown). The control unit 120 also executes processing using data stored in the storage unit 110 as needed, and / or causes the data generated by the execution of processing to be stored in the storage unit 110.

[0035] The display unit 130 is a display device that displays images (still images and / or moving images). That is, the display unit 130 includes, for example, a liquid crystal display or an organic electroluminescent (EL) display. Based on instructions from the control unit 120, the display unit 130 displays images so that they can be viewed by the user of the device 100.

[0036] The input unit 140 is an input device that receives input from the user of the device 100. That is, the input unit 140 includes, for example, a keyboard, mouse, touchpad, or touch panel. The input unit 140 outputs the data received from the user to the control unit 120.

[0037] The communication unit 150 is a communication device for communicating with other devices (for example, sending and receiving data). Specifically, the communication unit 150 includes, for example, a communication interface for wired communication and / or a communication interface for wireless communication. The communication unit 150 outputs data received from other devices to the control unit 120. The communication unit 150 also transmits data received from the control unit 120 to other devices.

[0038] Figure 2 is a block diagram showing an example of the main functions realized by the device 100. Figure 3 is a flowchart showing an example of steps performed in this method, performed by the device 100, or performed by the program on a computer.

[0039] In the example shown in Figure 2, the device 100 functionally includes a condition determination unit 10, a risk determination unit 20, and a display processing unit 30. In the example shown in Figure 3, the condition determination step S11, the risk determination step S21y or S21n, and the display processing step S30 are performed in this method, performed by the device 100, or executed by a computer based on this program.

[0040] In this method, first, one or more OCT images, preferably multiple OCT images, which are tomographic images of the patient's coronary arteries, are prepared. The multiple OCT images are acquired by using an OCT device to image multiple cross-sections of the coronary arteries at predetermined intervals (for example, intervals of 0.1 mm to 5 mm) over a predetermined length in the longitudinal direction of the patient's coronary arteries (for example, a length within the range of 10 mm to 200 mm).

[0041] The device 100 stores OCT image data, which is data for generating OCT images, in the storage unit 110. The device 100 acquires OCT image data from other devices such as OCT devices or other computers, for example, via the communication unit 150, and stores it in the storage unit 110. The control unit 120 of the device 100 can render the OCT image data to generate an OCT image and display it on the display unit 130.

[0042] In the condition determination step of this method, it is determined whether the atherosclerotic plaques visible in one or more OCT images satisfy the conditions: (a) the plaque has a fibrous capsule with a thickness below a predetermined threshold; and (b) the plaque contains cholesterol crystals (S11 in Figure 3).

[0043] In other words, in the condition determination step, it is first determined whether the atherosclerotic plaque visible in one OCT image corresponding to one cross-section of the coronary artery satisfies conditions (a) and (b). Here, the selection of the OCT image to be determined is not particularly limited as long as the effects of the present invention are obtained, but for example, it may be selected from among a plurality of OCT images based on instructions received from the user via the input unit 140 of the device 100.

[0044] In this embodiment, the atherosclerotic plaque to be determined is rich in lipids and is also called a lipid plaque. The lipid plaque appears in the OCT image as a portion where the intima of the coronary artery is thickened, accompanied by posterior attenuation, and the boundary on the outer side in the radial direction of the coronary artery is ambiguous.

[0045] That is, in the OCT image of a normal coronary artery, the media covers the outer side in the radial direction of the intima and appears as a layer with lower brightness than the intima. However, in the OCT image of a coronary artery with a lipid plaque, the media located on the outer side in the radial direction of the lipid plaque becomes invisible or the boundary between the media and the intima becomes ambiguous due to the posterior attenuation caused by the lipids in the lipid plaque.

[0046] Specifically, for example, in the OCT image shown in FIG. 8A described later, an atherosclerotic plaque, which is a lipid plaque, appears as a portion where the intima of the coronary artery is thickened, accompanied by posterior attenuation, and the boundary on the outer side in the radial direction of the coronary artery is ambiguous.

[0047] The atherosclerotic plaque shown in the OCT image is identified based on the image parameters of the OCT image (parameters characterizing each pixel constituting the OCT image). In this regard, the condition determination unit 10 of the present apparatus 100 identifies the atherosclerotic plaque shown in the OCT image to be determined based on the image parameters (for example, brightness) of the OCT image. That is, the condition determination unit 10 executes, for example, an image analysis process of comparing the image parameters of the OCT image with a predetermined criterion to identify the atherosclerotic plaque shown in the OCT image.

[0048] More specifically, the condition determination unit 10 identifies the atherosclerotic plaque shown in the OCT image as a region that includes the intima of the blood vessel and where the brightness of the region on the outer side in the radial direction is reduced due to posterior attenuation and the boundary with the region on the outer side in the radial direction is ambiguous (for example, the difference in brightness with the region on the outer side in the radial direction is smaller than a predetermined threshold).

[0049] Alternatively, the condition determination unit 10 may use a trained model that outputs the result of identifying the atheromas visible in the OCT image when an OCT image is input, to identify the atheromas visible in the OCT image.

[0050] Such trained models are generated, for example, by machine learning using OCT images showing atherosclerotic ulcers as training data. The condition determination unit 10 then inputs the OCT image to be determined into the trained model read from the storage unit 110, for example, and identifies the atherosclerotic ulcers shown in the OCT image based on the specific result output from the trained model.

[0051] The fibrous capsule that is the subject of the determination of whether condition (a) is met appears in the OCT image as a high-intensity layer covering the inner surface of the atherosclerotic plaque in the radial direction of the blood vessel. Specifically, for example, in the OCT image (1) shown on the left side of Figure 8A described later, the fibrous capsule appears as a high-intensity layer covering the inner surface of the atherosclerotic plaque in the radial direction of the coronary artery, as indicated by the arrow.

[0052] The fibrous coating visible in the OCT image is identified based on the image parameters of the OCT image. In this regard, the condition determination unit 10 of the device 100 identifies the fibrous coating visible in the OCT image based on the image parameters (e.g., brightness) of the OCT image to be determined. That is, the condition determination unit 10 performs image analysis processing that compares the image parameters of the OCT image with predetermined criteria and identifies the fibrous coating visible in the OCT image.

[0053] More specifically, the condition determination unit 10 identifies a fibrous capsule visible in the OCT image as a layered region covering at least a portion of the inner surface of the atherosclerotic plaque in the radial direction of the blood vessel, and having a higher brightness than the inner region (lumen of the blood vessel) and the outer region (lipidic plaque) in the radial direction, based on the image parameters of the OCT image.

[0054] Alternatively, the condition determination unit 10 may use a trained model that outputs the result of identifying the fibrous coating visible in the OCT image when an OCT image is input, to identify the fibrous coating visible in the OCT image.

[0055] Such trained models are generated, for example, by machine learning using OCT images showing atherosclerotic plaques with fibrous capsules as training data. The condition determination unit 10 then inputs the OCT image to be determined into the trained model read from the storage unit 110, for example, and identifies the fibrous capsule visible in the OCT image based on the identification result output from the trained model.

[0056] The condition determination unit 10 then compares the thickness of the fibrous coating shown in the OCT image with a predetermined threshold for condition (a) to determine whether the thickness of the fibrous coating is less than or equal to the threshold.

[0057] Specifically, the condition determination unit 10 first identifies the thickness of the fibrous capsule (i.e., the length of the fibrous capsule in the radial direction of the blood vessel) based on the image parameters of the OCT image (e.g., brightness) and data relating to the length in the OCT image (e.g., data relating the number of pixels to the scale of the subject), and then compares the length of the fibrous capsule with a predetermined threshold to determine whether the thickness of the fibrous capsule is less than or equal to the threshold.

[0058] Alternatively, the condition determination unit 10 may, upon receiving an OCT image, use a trained model that outputs a result of identifying the thickness of the fibrous coating visible in the OCT image to determine the thickness of the fibrous coating visible in the OCT image.

[0059] Such trained models are generated, for example, by machine learning using OCT images showing atherosclerotic plaques with fibrous capsules as training data. The condition determination unit 10 then inputs the OCT image to be determined into the trained model read from the storage unit 110, for example, and determines the thickness of the fibrous capsule shown in the OCT image based on the specific result output from the trained model.

[0060] The threshold for the thickness of the fibrous capsule used in condition (a) is not particularly limited as long as it is a value predetermined as suitable for the definition of thin-cap fibroatheroma (TCFA), which is a high-risk unstable plaque. For example, it may be a value in the range of 100 μm or less, preferably in the range of 80 μm or less, and particularly preferably in the range of 65 μm or less. Specifically, for example, 65 μm is particularly preferred as the threshold for the thickness of the fibrous capsule used in condition (a). That is, in the condition determination step, it is particularly preferable to determine whether the plaque satisfies condition (a), which is that it has a fibrous capsule with a thickness of 65 μm or less.

[0061] The cholesterol crystals (hereinafter referred to as "CCs") that are subject to the determination of whether or not condition (b) is met appear as regions with higher brightness than the surrounding area within the region where atherosclerotic plaques (lipid plaques) are visible in the OCT image, accompanied by particularly strong posterior attenuation (for example, stronger posterior attenuation than that caused by the lipid plaque), and having a linear shape extending in the circumferential direction of the blood vessel.

[0062] Specifically, for example, in the OCT image (2) shown on the right side of Figure 8A described later, as indicated by the arrowheads, at least two CCs appear as regions within the area of ​​atherosclerotic plaque (lipid plaque) that are more luminous than the surrounding area, exhibit particularly strong posterior attenuation, and have a linear shape extending circumferentially around the coronary artery.

[0063] In the condition determination step, it is determined whether the atherosclerotic plaques visible in the OCT image satisfy condition (b), that is, whether or not they contain CCs. CCs visible in the OCT image are identified based on the image parameters of the OCT image. In this regard, the condition determination unit 10 of the device 100 identifies the CCs visible in the OCT image based on the image parameters (e.g., brightness) of the OCT image to be determined. That is, the condition determination unit 10, for example, performs image analysis processing that compares the image parameters of the OCT image with a predetermined standard to identify the CCs visible in the OCT image.

[0064] More specifically, the condition determination unit 10 identifies CCs (Clinical Coagulations) visible in the OCT image as regions within the atherosclerosis that are brighter than the surrounding area, exhibit particularly strong posterior attenuation, and have a linear shape extending circumferentially around the coronary artery, based on the image parameters of the OCT image.

[0065] Alternatively, the condition determination unit 10 may use a trained model that outputs the result of identifying CCs in an OCT image when an OCT image is input to identify the CCs in the OCT image.

[0066] Such trained models are generated, for example, by machine learning using OCT images showing plaques containing CCs as training data. The condition determination unit 10 then inputs the OCT image to be determined into the trained model read from the storage unit 110, and identifies the CCs shown in the OCT image based on the identification result output from the trained model.

[0067] In the condition determination step, it may first be determined whether condition (a) is met, and if it is determined that condition (a) is met, then it may be determined whether condition (b) is met, or it may first be determined whether condition (b) is met, and if it is determined that condition (b) is met, then it may be determined whether condition (a) is met.

[0068] Furthermore, in the condition determination step, it is possible to determine whether the atherosclerotic plaques visible in a single OCT image selected from multiple OCT images satisfy conditions (a) and (b), or to determine whether the atherosclerotic plaques visible in multiple OCT images satisfy conditions (a) and (b) based on multiple OCT images.

[0069] In other words, for example, if it is determined that a plaque visible in one OCT image corresponding to one cross-section of a coronary artery satisfies one of conditions (a) and (b) but does not satisfy the other, then the vicinity of the imaging position of the OCT image in the longitudinal direction of the coronary artery (for example, 30 mm or less, 28 mm or less, 26 mm or less, 24 mm or less, 22 mm or less, 20 mm or less, 18 mm or less, 16 mm or less, 14 mm or less, 12 mm or less, 10 mm from the imaging position) It is determined whether atherosclerotic plaques visible in one or more other OCT images taken at a location within the range of m or less, 8 mm or less, 6 mm or less, 5 mm or less, 4 mm or less, 3 mm or less, 2 mm or less, or 1 mm or less satisfy the other of conditions (a) and (b). If atherosclerotic plaques visible in the other OCT images satisfy the other of conditions (a) and (b), then it may be determined that the atherosclerotic plaques visible in the multiple OCT images satisfy conditions (a) and (b). Note that the length of a single atherosclerotic plaque in the longitudinal direction of a blood vessel is often 30 mm or less.

[0070] In other words, in the condition determination step, if it is determined that the multiple OCT images subject to determination include a first OCT image showing a plaque that satisfies one of conditions (a) and (b), and a second OCT image showing a plaque that satisfies the other of conditions (a) and (b), then it may be determined that the plaques shown in the multiple OCT images satisfy conditions (a) and (b). In this case, the first OCT image and the second OCT image may be the same OCT image (i.e., one OCT image capturing the same vascular cross-section), or they may be different OCT images (i.e., two OCT images capturing two different vascular cross-sections).

[0071] Furthermore, in the condition determination step, if a plaque visible in one OCT image satisfies one of conditions (a) and (b) but not the other, it may be determined whether the plaque in other OCT images showing the same plaque satisfies the other of conditions (a) and (b). If the same plaque visible in the other OCT images satisfies the other of conditions (a) and (b), it may be determined that the plaque visible in these multiple OCT images satisfies conditions (a) and (b).

[0072] In this case, whether the atherosclerotic plaques visible in multiple OCT images are the same plaque is determined, for example, based on the proximity of the imaging locations of the multiple OCT images (for example, whether the distance between the imaging locations is below a predetermined threshold (for example, 30 mm or less, 28 mm or less, 26 mm or less, 24 mm or less, 22 mm or less, 20 mm or less, 18 mm or less, 16 mm or less, 14 mm or less, 12 mm or less, 10 mm or less, 8 mm or less, 6 mm or less, 5 mm or less, 4 mm or less, 3 mm or less, 2 mm or less, or 1 mm or less)) and / or whether the locations or areas of the atherosclerotic plaques visible in the multiple OCT images overlap with each other.

[0073] Furthermore, in the condition determination step, for example, even if it is determined that a plaque visible in one OCT image selected from multiple OCT images does not satisfy either condition (a) or (b), it is preferable to further determine whether a plaque visible in one or more other OCT images taken near the imaging position of the said OCT image satisfies the conditions (a) and (b).

[0074] In the risk assessment step of this method, if the atherosclerotic plaque visible in the OCT image is determined to satisfy conditions (a) and (b) in the condition assessment step described above (Yes in S11 of Figure 3), then the atherosclerotic plaque that satisfies conditions (a) and (b) is determined to be a higher-risk plaque compared to atherosclerotic plaque that does not satisfy one or both of conditions (a) and (b) (S21y in Figure 3).

[0075] In other words, in the risk assessment step, if an atherosclerotic plaque visible in the OCT image is determined to have a fibrous capsule with a thickness below a predetermined threshold and to contain CCs, then that atherosclerotic plaque is determined to be of higher risk than an atherosclerotic plaque that does not have a fibrous capsule with a thickness below the threshold and / or does not contain CCs.

[0076] In this case, the risk determination unit 20 of the device 100 generates determination result data (data containing information that the plaque in the OCT image is a high-risk plaque) indicating that the plaque shown in the OCT image is a high-risk plaque, and stores it in the storage unit 110. The display processing unit 30 of the device 100 then displays the determination result (for example, an image showing that the plaque in the OCT image is a high-risk plaque) on the display unit 130 based on the determination result data generated by the risk determination unit 20 (S31 in Figure 3). As a result, the user of the device 100 can recognize that the plaque that was the subject of the determination is a high-risk plaque.

[0077] On the other hand, in the condition determination step, if it is determined that the atherosclerotic plaque visible in the OCT image does not meet one or both of conditions (a) and (b) (No in S11 of Figure 3), then in the risk determination step, it is determined that the atherosclerotic plaque is not a high-risk plaque (S21n in Figure 3).

[0078] In this case, the risk determination unit 20 of the device 100 generates determination result data indicating that the atherosclerotic cysts visible in the OCT image are not high-risk cysts, and stores it in the storage unit 110. Then, the display processing unit 30 of the device 100 displays the determination result that the atherosclerotic cysts visible in the OCT image are not high-risk cysts on the display unit 130 based on the determination result data generated by the risk determination unit 20 (S31 in Figure 3). As a result, the user of the device 100 can recognize that the atherosclerotic cysts that were the subject of the determination are not high-risk cysts.

[0079] According to the present invention, it is possible to determine with higher accuracy than before whether or not an atherosclerotic plaque visible in an OCT image is a high-risk atherosclerotic plaque. In other words, as described above, it has been known that thin-cap fibrous atherosclerotic plaque (TCFA) is an unstable atherosclerotic plaque that carries a high risk of causing acute myocardial infarction in the future.

[0080] However, conventionally, the gold standard method for identifying whether or not a plaque is TCFA has been based on histopathological images obtained after the patient's death, and it has been difficult to accurately identify TCFA based on OCT images obtained while the patient is alive.

[0081] In other words, conventionally, in OCT images, if the thickness of the fibrous capsule of a plaque was below a predetermined threshold, it was sometimes determined that the plaque was TCFA. However, as demonstrated in the examples described later, there was a problem with the low positive predictive value when the determination was based solely on the thickness of the fibrous capsule.

[0082] In contrast, the inventors of the present invention have uniquely discovered that, as demonstrated in the examples described below, by using the condition (b) that the atherosclerotic ulcer contains CCs in addition to the condition (a) that the thickness of the fibrous capsule of the atherosclerotic ulcer is below a predetermined threshold as an identification criterion, high-risk atherosclerotic ulcers, or true TCFAs, can be identified with higher accuracy than conventional methods.

[0083] In this regard, in the risk assessment step of this method, atherosclerotic plaques that satisfy conditions (a) and (b) may be determined to have a higher risk of developing vascular disease or complications during treatment (i.e., a higher risk of developing vascular disease or complications during treatment) compared to atherosclerotic plaques that do not satisfy one or both of conditions (a) and (b).

[0084] In this case, the risk determination unit 20 of the device 100 generates determination result data indicating that the atherosclerotic plaque visible in the OCT image is a plaque with a high risk of causing vascular disease or complications during treatment, and stores it in the storage unit 110. The display processing unit 30 of the device 100 then displays the determination result that the atherosclerotic plaque visible in the OCT image is a plaque with a high risk of causing vascular disease or complications during treatment on the display unit 130 based on the determination result data generated by the risk determination unit 20 (S31 in Figure 3). As a result, the user of the device 100 can recognize that the atherosclerotic plaque that was the subject of the determination is a plaque with a high risk of causing vascular disease or complications during treatment.

[0085] Vascular diseases that may arise from atherosclerotic plaques that satisfy conditions (a) and (b) include, for example, coronary artery disease (e.g., acute myocardial infarction, angina pectoris, or other diseases resulting from occlusion or stenosis of the coronary arteries), peripheral artery disease (e.g., arteriosclerosis obliterans, acute arterial occlusion, or other diseases resulting from occlusion or stenosis of peripheral blood vessels), and cerebrovascular disease (carotid artery stenosis, intracranial artery stenosis, atherothrombotic cerebral infarction, or other diseases resulting from occlusion or stenosis of cerebral blood vessels).

[0086] Complications that may occur during treatment due to atherosclerotic plaques that meet conditions (a) and (b) include, for example, complications during catheter treatment. Complications during catheter treatment may include, for example, one or more selected from the group consisting of perioperative myocardial infarction, microembolism (e.g., microembolism in one or more locations selected from the group consisting of coronary arteries, peripheral blood vessels and cerebral blood vessels), and delayed contrast enhancement.

[0087] If atherosclerotic plaques visible in OCT images are determined to be high-risk, the treatment plan, examination plan, and management plan for the patient can be appropriately determined, taking that risk into consideration.

[0088] In other words, for example, when performing coronary artery catheter treatment involving a high-risk atherosclerotic plaque, it is possible to adopt a strategy of performing balloon catheter dilation or stent placement under conditions that make it difficult for the atherosclerotic plaque to rupture, and / or positioning an intravascular device (e.g., a filter wire) downstream of the atherosclerotic plaque to capture the fragments, taking into consideration the risk that the atherosclerotic plaque may rupture during the catheter treatment and fragments may leak into the bloodstream, causing problems such as peripheral embolism downstream.

[0089] Furthermore, for example, prior to catheter treatment, it is possible to prevent the destruction of high-risk atherosclerotic plaques during the procedure by using an excimer laser to pre-burn them.

[0090] Furthermore, if, for example, a high-risk atherosclerosis is found in a different location than the one originally planned as the target for catheter treatment, the treatment plan can be changed to include the area containing the high-risk atherosclerosis as part of the target for catheter treatment.

[0091] Furthermore, for example, the presence of high-risk atherosclerotic plaques may increase the likelihood of developing new vascular diseases in the future, allowing for the appropriate design of subsequent treatment plans for the patient, including drug therapy.

[0092] In this method, it is also possible to determine whether an atherosclerotic plaque determined to satisfy the above-described conditions (a) and (b) also satisfies additional conditions. That is, in this method, for example, if an atherosclerotic plaque determined to satisfy conditions (a) and (b) in the condition determination step further satisfies: (c1) the representative value of the number of CCs contained in the atherosclerotic plaque is greater than or equal to a predetermined threshold; (c2) the representative value of the number of layers of CCs in the radial direction of the blood vessel is greater than or equal to a predetermined threshold; (c3) the shortest distance between the CCs and the inner surface of the blood vessel is less than or equal to a predetermined threshold; (d1) the representative value of the distribution angle of the atherosclerotic plaque in the cross-section of the blood vessel is greater than or equal to a predetermined threshold; and (d2) the atherosclerotic plaque in the longitudinal direction of the blood vessel The procedure may further include: an additional condition determination step of determining whether the plaque satisfies one or more additional conditions selected from the group consisting of: the length of the plaque is greater than or equal to a predetermined threshold; and an additional risk determination step of determining whether, if the plaque is determined to satisfy one or more of the additional conditions, a specific plaque that satisfies conditions (a) and (b) and one or more of the additional conditions is a higher-risk plaque than a plaque that satisfies conditions (a) and (b) and does not satisfy the additional conditions that the specific plaque satisfies.

[0093] In other words, as described above, the inventors of the present invention have found that atherosclerotic plaques satisfying conditions (a) and (b) are high-risk atherosclerotic plaques, and have also independently discovered that atherosclerotic plaques satisfying one or more additional conditions selected from the group consisting of (c1), (c2), (c3), (d1), and (d2) above, in addition to conditions (a) and (b), are particularly high-risk atherosclerotic plaques.

[0094] This method, which determines based on additional conditions relating to the number and / or arrangement of CCs, determines whether atherosclerotic plaque (hereinafter sometimes referred to as "true TCFA") that is determined to satisfy conditions (a) and (b) in the condition determination step also satisfies one or more additional conditions selected from the group consisting of: (c1) the representative value of the number of CCs in the true TCFA is greater than or equal to a predetermined threshold; (c2) the representative value of the number of layers of CCs in the radial direction of the blood vessel is greater than or equal to a predetermined threshold; and (c3) the shortest distance between the CCs and the inner surface of the blood vessel is less than or equal to a predetermined threshold. The method further includes an additional condition determination step for determining whether a true TCFA satisfies one or more of the additional conditions, and an additional risk determination step for determining that a true TCFA that satisfies conditions (a) and (b) and one or more of the additional conditions (hereinafter sometimes referred to as a "specific TCFA") is a higher-risk plaque than a plaque that satisfies conditions (a) and (b) and does not satisfy the additional conditions that the specific TCFA satisfies (i.e., a true TCFA that does not satisfy the additional conditions that the specific TCFA satisfies). Figure 4 is a flowchart showing an example of steps performed in this method, performed by the device, or performed by the program on a computer.

[0095] Specifically, in the additional condition step for determining the additional condition (c1), it is determined whether the representative value of the number of CCs included in the true TCFA is greater than or equal to a predetermined threshold (S12 in Figure 4). In this case, the condition determination unit 10 of the device 100 counts the number of CCs present in the region where the true TCFA is visible in the OCT image, compares the representative value of that number with a predetermined threshold, and determines whether the representative value is greater than or equal to the threshold.

[0096] Here, if one OCT image is used as the OCT image to be judged, the number of CCs contained in the true TCFAs shown in that OCT image is determined as the representative value of the number of CCs. If multiple OCT images are used as the OCT images to be judged, the number of CCs contained in the true TCFAs shown in each of the multiple OCT images is counted, and for example, the maximum or average number of CCs in the multiple OCT images is determined as the representative value of the number of CCs.

[0097] The threshold used in the additional condition (c1) is not particularly limited as long as the effects of the present invention are obtained, and may be appropriately determined according to the representative value. For example, it may be determined whether the maximum number of CCs included in the true TCFA is 2 or more, 3 or more, 4 or more, or 5 or more.

[0098] As described above, in OCT images, CCs contained in atherosclerotic plaques are identified as regions that are brighter than the surrounding area, exhibit particularly strong posterior attenuation, and have a linear shape extending in the circumferential direction of the blood vessel. Therefore, the condition determination unit 10 of this device 100 identifies the CCs visible in the OCT image based on the image parameters of the OCT image to be determined, counts the number of CCs, and determines a representative value.

[0099] Alternatively, the condition determination unit 10 may, upon receiving an OCT image, determine a representative value for the number of CCs in the OCT image using a trained model that outputs the result of counting the number of CCs in the OCT image.

[0100] Such trained models are generated, for example, by machine learning using OCT images showing plaques containing CCs as training data. The condition determination unit 10 then inputs the OCT image to be determined into the trained model read from the storage unit 110, and determines a representative value for the number of CCs shown in the OCT image based on the count result output from the trained model.

[0101] In the additional risk assessment step, if a true TCFA is determined to meet additional condition (c1) (Yes in S12 of Figure 4), then the specific TCFA that is a true TCFA meeting additional condition (c1) is determined to be a higher-risk atherosclerosis than a true TCFA that does not meet additional condition (c1) (S22y in Figure 4). Note that the larger the threshold used for additional condition (c1), the higher the risk of atherosclerosis a true TCFA that meets additional condition (c1) is determined to be.

[0102] In the additional condition step for determining the additional condition (c2), it is determined whether the representative value of the number of radial layers of blood vessels of CCs included in the true TCFA is greater than or equal to a predetermined threshold (S12 in Figure 4). In this case, the condition determination unit 10 of the device 100 counts the number of radial layers of blood vessels of CCs present in the region where the true TCFA is visible in the OCT image, compares the representative value of this number with a predetermined threshold, and determines whether the representative value is greater than or equal to the threshold.

[0103] Here, when using one OCT image as the OCT image to be judged, the number of radial layers of CCs in the blood vessels of the true TCFA shown in that OCT image is counted, and for example, the maximum or average number of CCs layers in that OCT image is determined as a representative value for the number of CCs layers. Furthermore, when using multiple OCT images as the OCT images to be judged, the number of radial layers of CCs in the blood vessels of the true TCFA shown in each of the multiple OCT images is counted, and for example, the maximum or average number of CCs layers in the multiple OCT images is determined as a representative value for the number of CCs layers.

[0104] The threshold used in additional condition (c2) is not particularly limited as long as the effects of the present invention are obtained, and may be appropriately determined according to the representative value. For example, it may be determined whether the maximum number of radial layers of blood vessels of CCs included in true TCFA is 2 or more, or 3 or more.

[0105] Here, the number of CC layers is determined as the number of CCs that overlap in the radial direction of the blood vessel within the true TCFA. That is, for example, in an OCT image, multiple CCs that lie on a straight line drawn from the center point of the blood vessel outward in the radial direction of the blood vessel are identified as CCs that form a layer in the radial direction of the blood vessel, and the number of such multiple CCs is determined as the number of layers of such CCs in the radial direction of the blood vessel.

[0106] In OCT images, the boundaries of multiple CCs forming layers in the radial direction of a blood vessel are identified as regions with lower brightness compared to the CCs themselves. Furthermore, the center point of a blood vessel in an OCT image is determined as either the centroid of the lumen of the blood vessel in that OCT image, or the center point of the OCT catheter visible in that OCT image.

[0107] Therefore, the condition determination unit 10 of the device 100 identifies the CCs (concentrated vascular cells) visible in the OCT image based on the image parameters of the OCT image to be determined, counts the number of layers of the CCs in the radial direction of the blood vessel, and determines a representative value.

[0108] Alternatively, the condition determination unit 10 may, upon receiving an OCT image, use a trained model that outputs the result of counting the number of layers in the radial direction of the blood vessels of the CCs shown in the OCT image to determine a representative value for the number of layers in the radial direction of the blood vessels of the CCs shown in the OCT image.

[0109] Such trained models are generated, for example, by machine learning using OCT images as training data that show atherosclerotic plaques containing multiple CCs forming layers in the radial direction of the blood vessels. The condition determination unit 10 then inputs the OCT image to be determined into the trained model read from the storage unit 110, and determines a representative value for the number of layers of CCs shown in the OCT image based on the count result output from the trained model.

[0110] In the additional risk assessment step, if a true TCFA is determined to satisfy additional condition (c2) (Yes in S12 of Figure 4), then the specific TCFA that satisfies additional condition (c2) is determined to be a higher-risk atherosclerosis than a true TCFA that does not satisfy additional condition (c2) (S22y in Figure 4). Note that the larger the threshold used for additional condition (c2), the higher the risk of atherosclerosis a true TCFA that satisfies additional condition (c2) is determined to be.

[0111] In the additional condition step for determining the additional condition (c3), it is determined whether the shortest distance between CCs included in the true TCFA and the inner surface of the blood vessel is less than or equal to a predetermined threshold (S12 in Figure 4). In this case, the condition determination unit 10 of the device 100 determines the shortest distance between CCs located in the region where the true TCFA is visible in the OCT image and the inner surface of the blood vessel, compares this shortest distance with a predetermined threshold, and determines whether the shortest distance is less than or equal to the threshold.

[0112] Here, when using one OCT image as the OCT image to be judged, the minimum distance between each of the one or more CCs contained in the true TCFA shown in that OCT image and the inner surface of the blood vessel is determined as the shortest distance between the CCs and the inner surface of the blood vessel. Furthermore, when using multiple OCT images as the OCT images to be judged, the minimum distance between each of the one or more CCs contained in the true TCFA shown in each of the multiple OCT images and the inner surface of the blood vessel is determined as the shortest distance between the CCs and the inner surface of the blood vessel.

[0113] The threshold used in additional condition (c3) is not particularly limited as long as the effects of the present invention are obtained, but for example, it may be determined by whether the shortest distance between the CCs contained in true TCFA and the inner surface of the blood vessel is below a threshold within the range of 300 μm or less, 290 μm or less, 280 μm or less, 270 μm or less, 260 μm or less, 250 μm or less, 240 μm or less, 230 μm or less, 220 μm or less, 210 μm or less, 200 μm or less, 190 μm or less, 180 μm or less, 170 μm or less, 160 μm or less, 150 μm or less, 140 μm or less, 130 μm or less, 120 μm or less, 110 μm or less, 100 μm or less, 90 μm or less, 80 μm or less, 70 μm or less, or 60 μm or less.

[0114] In OCT images, the lumen of a blood vessel appears as a low-brightness central region surrounded by the vessel wall; therefore, the inner surface of the blood vessel is identified as the boundary between the lumen and the vessel wall. Specifically, for example, in the OCT images shown in Figures 14A and 14B, the thickness of the medium-brightness region between the high-brightness CCs (arrowheads) and the low-brightness coronary artery lumen is determined as the shortest distance between the CCs and the inner surface of the coronary artery.

[0115] Therefore, the condition determination unit 10 of the device 100 identifies true CCs within the TCFA visible in the OCT image based on the image parameters of the OCT image to be determined, and determines the shortest distance between the CCs and the inner surface of the blood vessel.

[0116] Alternatively, the condition determination unit 10 may, upon receiving an OCT image, use a trained model that outputs the result of determining the shortest distance between the CCs (Cell Capsules) visible in the OCT image and the inner surface of the blood vessel to determine the shortest distance between the CCs and the inner surface of the blood vessel.

[0117] Such trained models are generated, for example, by machine learning using OCT images showing plaques containing CCs as training data. The condition determination unit 10 then inputs the OCT image to be determined into the trained model read from the storage unit 110, and determines the shortest distance between the CCs shown in the OCT image and the inner surface of the blood vessel based on the determination result output from the trained model.

[0118] In the additional risk assessment step, if a true TCFA is determined to meet additional condition (c3) (Yes in S12 of Figure 4), then the specific TCFA that is a true TCFA meeting additional condition (c3) is determined to be a higher-risk atherosclerosis than a true TCFA that does not meet additional condition (c3) (S22y in Figure 4). Note that the smaller the threshold used for additional condition (c3), the higher-risk atherosclerosis a TCFA that meets additional condition (c3) is determined to be.

[0119] Thus, in the additional risk assessment step, if it is determined that a true TCFA satisfies one, two ((c1) and (c2), (c1) and (c3), or (c2) and (c3)), or three of the additional conditions (c1), (c2), and (c3) (Yes in S12 of Figure 4), then the specific TCFA, which is a true TCFA that satisfies one or more of the additional conditions, is determined to be a plaque with a higher risk (i.e., a true TCFA with a particularly high risk) compared to a true TCFA that does not satisfy the one, two, or three additional conditions that the specific TCFA satisfies (S22y in Figure 4).

[0120] In this case, the risk determination unit 20 of the device 100 generates determination result data indicating that the atherosclerotic cysts visible in the OCT image are of particularly high risk and stores it in the storage unit 110. The display processing unit 30 of the device 100 then displays the determination result that the atherosclerotic cysts visible in the OCT image are of particularly high risk on the display unit 130 based on the determination result data generated by the risk determination unit 20 (S31 in Figure 4). As a result, the user of the device 100 can recognize that the atherosclerotic cysts visible in the OCT image that were the subject of the determination are of particularly high risk.

[0121] On the other hand, if in the additional condition determination step it is determined that the true TCFA does not satisfy any of the above additional conditions (c1), (c2), and (c3) (No in S12 of Figure 4), then in the additional risk determination step it is determined that the true TCFA is a plaque with a higher risk than a plaque that does not satisfy one or both of the above conditions (a) and (b) (S22n in Figure 4). In this case, in the additional risk determination step it may be determined that the plaque visible in the OCT image is a true TCFA, but not a true TCFA with a particularly high risk (specific TCFA).

[0122] In the condition determination step (S11 in Figure 4) in which it is determined whether the atherosclerotic plaque visible in the OCT image meets conditions (a) and (b), if it is determined that one or both of conditions (a) and (b) are not met (No in S11 in Figure 4), then the atherosclerotic plaque is determined not to be a high-risk atherosclerotic plaque (S21n in Figure 4).

[0123] The method for determining based on additional conditions relating to the size and / or location of atherosclerotic plaques further includes an additional condition determination step for determining whether an atherosclerotic plaque (true TCFA) determined to satisfy conditions (a) and (b) in the condition determination step also satisfies one or more additional conditions selected from the group consisting of: (d1) the representative value of the distribution angle of the true TCFA in the cross-section of the blood vessel is greater than or equal to a predetermined threshold; and (d2) the length of the true TCFA in the longitudinal direction of the blood vessel is greater than or equal to a predetermined threshold; and an additional risk determination step for determining if the true TCFA is determined to satisfy one or more of the additional conditions, and if the true TCFA is determined to satisfy conditions (a) and (b) and one or more of the additional conditions, then the specific TCFA which is the true TCFA satisfying conditions (a) and (b) and one or more of the additional conditions is a higher-risk atherosclerotic plaque than an atherosclerotic plaque which satisfies conditions (a) and (b) but does not satisfy the additional conditions (i.e., a true TCFA which does not satisfy the additional conditions that the specific TCFA satisfies). Figure 5 is a flowchart showing an example of steps performed in this method, performed by this device, or executed by this program on a computer.

[0124] Specifically, in the additional condition step for determining the additional condition (d1), it is determined whether the representative value of the distribution angle of true TCFA in the cross-section of the blood vessel is greater than or equal to a predetermined threshold (S13 in Figure 5). In this case, the condition determination unit 10 of the device 100 determines the distribution angle of true TCFA in the cross-section of the blood vessel as seen in the OCT image, compares the representative value of the distribution angle with a predetermined threshold, and determines whether the distribution angle is greater than or equal to the threshold.

[0125] Here, if one OCT image is used as the OCT image to be judged, the distribution angle of the true TCFA shown in that OCT image is determined as the representative value of the distribution angle of the true TCFA. If multiple OCT images are used as the OCT images to be judged, the distribution angle of the true TCFA shown in each of the multiple OCT images is determined, and for example, the maximum or average value of the distribution angles in the multiple OCT images is determined as the representative value of the distribution angle of the true TCFA.

[0126] The threshold for the distribution angle used in the additional condition (d1) is not particularly limited as long as the effects of the present invention are obtained, and may be appropriately determined according to the representative value. For example, it may be determined whether the maximum value of the true TCFA distribution angle in the cross-section of the blood vessel is above a threshold within the range of 90° or more, 100° or more, 120° or more, 140° or more, 160° or more, 180° or more, 200° or more, 220° or more, 240° or more, 260° or more, or 270° or more.

[0127] In the OCT image, true TCFA is identified as atherosclerotic plaque that satisfies conditions (a) and (b) as described above. Therefore, the condition determination unit 10 of the device 100 first identifies one end and the other end of the true TCFA in the circumferential direction of the blood vessel in the OCT image, determines the center point of the blood vessel, and determines the angle formed by the line segment connecting the center point and the one end and the line segment connecting the center point and the other end as the distribution angle of the true TCFA. Here, the center point of the blood vessel in the OCT image is determined as the centroid of the lumen of the blood vessel in the OCT image, or the center point of the OCT catheter shown in the OCT image.

[0128] Alternatively, the condition determination unit 10 may, upon receiving an OCT image, determine a representative value of the distribution angle of the true TCFAs shown in the OCT image using a trained model that outputs the result of determining the distribution angle of the true TCFAs shown in the OCT image.

[0129] Such a trained model is generated, for example, by machine learning using an OCT image showing true TCFA as training data. The condition determination unit 10 then inputs the OCT image to be determined into the trained model read from the storage unit 110, and determines a representative value of the distribution angle of true TCFA shown in the OCT image based on the decision result output from the trained model.

[0130] In the additional risk assessment step, if a true TCFA is determined to meet additional condition (d1), that specific TCFA, which is a true TCFA that meets additional condition (d1), is determined to be a higher-risk atherosclerosis than a true TCFA that does not meet additional condition (d1) (S23y in Figure 5). Note that the larger the threshold used for additional condition (d1), the higher the risk of atherosclerosis that a TCFA meeting additional condition (d1) is determined to be.

[0131] In the additional condition step for determining the additional condition (d2), it is determined whether the length of the true TCFA in the longitudinal direction of the blood vessel is greater than or equal to a predetermined threshold (S13 in Figure 5). In this case, the condition determination unit 10 of the device 100 determines the length of the true TCFA in the longitudinal direction of the blood vessel as seen in the OCT image, compares this length with a predetermined threshold, and determines whether the length is greater than or equal to the threshold.

[0132] Here, the length of the true TCFA in the longitudinal direction of the blood vessel is determined as the imaging range of multiple OCT images in which the true TCFA is captured (specifically, the distance from the imaging position of the OCT image at one end of the blood vessel in the longitudinal direction to the imaging position of the OCT image at the other end of the blood vessel in the longitudinal direction, among the multiple OCT images in which the true TCFA is captured within a predetermined range of the blood vessel).

[0133] The length threshold used in the additional condition (d2) is not particularly limited as long as the effects of the present invention are obtained, but for example, it is determined by whether the true length of the TCFA in the longitudinal direction of the blood vessel is greater than or equal to a threshold within the range of 1 mm or more, 2 mm or more, 3 mm or more, 4 mm or more, 5 mm or more, 6 mm or more, 7 mm or more, 8 mm or more, 9 mm or more, or 10 mm or more.

[0134] Furthermore, the condition determination unit 10 may, upon receiving multiple OCT images, use a trained model that outputs the result of determining the longitudinal length of the atherosclerotic blood vessels that are commonly seen in the multiple OCT images to identify the true TCFA length that is commonly seen in the multiple OCT images.

[0135] Such a trained model is generated, for example, by machine learning using OCT images showing atherosclerotic plaques as training data. The condition determination unit 10 then inputs multiple OCT images to be determined into the trained model read from the storage unit 110, and determines the longitudinal length of the blood vessels of the atherosclerotic plaques shown in the multiple OCT images based on the determination result output from the trained model.

[0136] In the additional risk assessment step, if a true TCFA is determined to meet additional condition (d2), that specific TCFA, which is a true TCFA that meets additional condition (d2), is determined to be a higher-risk atherosclerosis than a true TCFA that does not meet additional condition (d2) (S23y in Figure 5). Note that the larger the threshold used for additional condition (d2), the higher the risk of atherosclerosis that a TCFA meeting additional condition (d2) is determined to be.

[0137] Thus, in the additional risk assessment step, if it is determined that a true TCFA satisfies one or two of the additional conditions (d1) and (d2) (Yes in S13 of Figure 5), the specific TCFA that is a true TCFA satisfying one or more of the additional conditions is determined to be a plaque with a higher risk (i.e., a true TCFA with a particularly high risk) compared to a true TCFA that does not satisfy one or two of the additional conditions that the specific TCFA satisfies (S23y in Figure 5).

[0138] In this case, the risk determination unit 20 of the device 100 generates determination result data indicating that the atherosclerotic ulcers visible in the OCT image are of particularly high risk and stores it in the storage unit 110. The display processing unit 30 of the device 100 then displays the determination result that the atherosclerotic ulcers visible in the OCT image are of particularly high risk on the display unit 130 based on the determination result data generated by the risk determination unit 20 (S31 in Figure 5). As a result, the user of the device 100 can recognize that the atherosclerotic ulcers visible in the OCT image that were the subject of the determination are of particularly high risk.

[0139] On the other hand, if in the additional condition determination step it is determined that the true TCFA does not satisfy either of the above additional conditions (d1) and (d2) (No in S13 of Figure 5), then in the additional risk determination step it is determined that the true TCFA is a plaque with a higher risk than a plaque that does not satisfy one or both of the above conditions (a) and (b) (S13n in Figure 5). In this case, in the additional risk determination step it may be determined that the plaque visible in the OCT image is a true TCFA, but is not a true TCFA with a particularly high risk (specific TCFA).

[0140] In the condition determination step (S11 in Figure 5) in which it is determined whether the atherosclerotic plaque visible in the OCT image meets conditions (a) and (b), if it is determined that one or both of conditions (a) and (b) are not met (No in S11 in Figure 5), then the atherosclerotic plaque is determined not to be a high-risk atherosclerotic plaque (S21n in Figure 5).

[0141] According to the method of performing the determination using the additional conditions (c1), (c2), (c3), (d1), or (d2) described above, it is possible to determine with high accuracy whether the true TCFAs shown in the OCT images are true TCFAs with particularly high risk (specific TCFAs). Therefore, in this case, it is possible to more appropriately determine the patient's treatment plan, examination plan, and management plan, taking into account future risks.

[0142] Next, a specific example of this embodiment will be described.

[0143] Using OCT images and histopathological images of the coronary arteries of patients who gave written consent during their lifetime or who gave written consent from their bereaved families after their death, stored at the National Cerebral and Cardiovascular Center (Osaka, Japan), the positive predictive value, negative predictive value, sensitivity, and specificity for determining whether or not TCFA is present in the coronary arteries were evaluated.

[0144] Specifically, for each of 20 patients who died from vascular diseases such as myocardial infarction and cerebral infarction, post-mortem OCT images of the coronary arteries and post-mortem autopsy histopathological images of the coronary arteries were used. First, for each patient, one histopathological image showing atherosclerotic plaque was selected, and one OCT image taken near the cross-section of the coronary artery corresponding to that histopathological image was selected as the reference OCT image.

[0145] Next, in Example 1, it was determined whether the atherosclerotic ulcers visible in the OCT image met the following conditions: (a) the ulcers have a fibrous capsule with a thickness of 65 μm or less; and (b) the ulcers contain CCs. Specifically, it was first determined whether the atherosclerotic ulcers visible in the reference OCT image had a fibrous capsule with a thickness of 65 μm or less.

[0146] Furthermore, if the atherosclerotic ulcer visible in the reference OCT image was found to have a fibrous capsule with a thickness of 65 μm or less, it was determined whether or not CCs were present within the atherosclerotic ulcer visible in the reference OCT image.

[0147] In this context, if a plaque with a fibrous capsule less than 65 μm thick was found to contain CCs in the reference OCT image, the plaque was determined to satisfy conditions (a) and (b) and to be TCFA.

[0148] On the other hand, if the reference OCT image did not show any plaques with a fibrous capsule of 65 μm or less inclusion of CCs, then one or more other OCT images acquired within a 1 mm radius in the longitudinal direction of the coronary artery from the cross-section of the coronary artery corresponding to the reference OCT image (i.e., within a total of 2 mm radius centered on the cross-section of the coronary artery corresponding to the reference OCT image) were selected as nearby OCT images, and it was determined whether or not CCs were present in the plaques visible in these nearby OCT images. If CCs were found to be present in the plaques visible in the nearby OCT images, then it was determined that condition (b) was also met.

[0149] On the other hand, in Example C1, only whether the atherosclerotic ulcer visible in the OCT image met condition (a) above was determined, and whether it met condition (b) above was not determined. Specifically, it was determined whether the atherosclerotic ulcer visible in the reference OCT image had a fibrous capsule with a thickness of 65 μm or less, and if it was determined that the atherosclerotic ulcer had a fibrous capsule with a thickness of 65 μm or less, then the atherosclerotic ulcer met condition (a) above and was determined to be TCFA.

[0150] Subsequently, experienced physicians determined whether the atherosclerotic plaques in each patient's coronary arteries were actually TCFAs using histopathological images, which are used as the gold standard for identifying TCFAs.

[0151] Then, for each of Example 1 and Example C1, the number of patients in whom the atheroma was determined to be TCFA by OCT imaging and also determined to be TCFA by pathological tissue imaging is defined as "a", the number of patients in whom the atheroma was determined not to be TCFA by OCT imaging but was determined to be TCFA by pathological tissue imaging is defined as "b", and the number of patients in whom the atheroma was determined to be TCFA by OCT imaging but was determined to be TCFA by pathological tissue imaging is defined as "a". Let "c" be the number of patients whose atherosclerotic ulcers were determined not to be TCFA, and "d" be the number of patients whose atherosclerotic ulcers were determined not to be TCFA by OCT imaging and also by pathological tissue imaging. The positive predictive value, negative predictive value, sensitivity, and specificity were calculated using the following formulas: Positive predictive value (%) = a / (a ​​+ c) × 100; Negative predictive value (%) = d / (b + d) × 100; Sensitivity (%) = a / (a ​​+ b) × 100; Specificity (%) = d / (c + d) × 100.

[0152] Figure 6 shows the calculation results for positive predictive value, negative predictive value, sensitivity, and specificity. As shown in Figure 6, in Example C1, where only whether condition (a) was met was determined by OCT image analysis, the negative predictive value was 100%, the sensitivity was 100%, and the specificity was 96%, but the positive predictive value was 62%.

[0153] In other words, in example C1, for instance, there was a case where an area that was determined to be a fibrous capsule with a thickness of 65 μm or less based on OCT imaging was actually found to be a layer infiltrated by macrophages based on histopathological imaging.

[0154] In contrast, in Example 1, where the determination was made based on whether both conditions (a) and (b) were met using OCT images, the negative predictive value, sensitivity, and specificity were not significantly different from those of Example C1, but the positive predictive value was 96%, which was significantly higher than that of Example C1.

[0155] In other words, in the determination of TCFA using OCT images, by determining whether or not the atherosclerotic plaque contains CCs, in addition to the thickness of the fibrous capsule, the accuracy of identifying TCFA was significantly improved compared to when only the thickness of the fibrous capsule was determined.

[0156] Using OCT images of coronary arteries from patients who have given written consent, and lipid content measurement data obtained using near-infrared spectroscopy (NIRS), which are stored at the National Cerebral and Cardiovascular Center (Osaka, Japan), the correlation between the TCFA determination results from the OCT images and the lipid content within atherosclerotic plaques was evaluated.

[0157] Specifically, for each of the 159 patients diagnosed with arteriosclerosis, it was determined whether the plaques visible on the coronary artery OCT images met the following conditions: (a) the plaque has a fibrous capsule with a thickness of 65 μm or less; and (b) the plaque contains CCs. The correlation between this determination and the lipid content index of the plaque measured using a NIRS device was then evaluated. It is known that plaques in the coronary arteries are more unstable and have a higher likelihood of causing acute myocardial infarction in the future if they have a higher lipid content.

[0158] Figure 7 shows the evaluation results of the lipid content index of atherosclerotic ulcers. On the horizontal axis of Figure 7, "TCFA (-)" indicates that condition (a) was not met, "TCFA (+)" indicates that condition (a) was met, "CCs (-)" indicates that condition (b) was not met, and "CCs (+)" indicates that condition (b) was met.

[0159] As shown in Figure 7, the lipid content index of atherosclerotic ulcers was 355.9 for ulcers (TCFA(-)CCs(-)) that were determined not to meet both conditions (a) and (b), 507.7 for ulcers (TCFA(-)CCs(+)) that were determined not to meet condition (a) but to meet condition (b), and 476.5 for ulcers (TCFA(+)CCs(-)) that were determined to meet condition (a) but not condition (b).

[0160] In contrast, the lipid content index of atheromas (TCFA(+)CCs(+)) that were determined to meet both conditions (a) and (b) was 697.0. In other words, the lipid content index of atheromas determined to meet both conditions (a) and (b) in the OCT image assessment was significantly higher than that of atheromas that were determined not to meet one or both of the conditions (a) and (b).

[0161] Therefore, atherosclerotic plaques determined to satisfy conditions (a) and (b) based on OCT imaging were considered to be more unstable and more likely to cause acute myocardial infarction in the future compared to atherosclerotic plaques determined not to satisfy one or both of conditions (a) and (b).

[0162] For each of the 159 patients evaluated in Example 2 described above, the correlation between the determination of whether the atherosclerotic plaque visible in the coronary artery OCT image met the following conditions: (a) the plaque has a fibrous capsule with a thickness of 65 μm or less; and (b) the plaque contains CCs, and whether or not contrast enhancement delay was observed during catheter treatment in the coronary artery was evaluated. It is known that when contrast enhancement delay is observed during catheter treatment, the risk of complications such as perioperative myocardial infarction occurring during the catheter treatment is higher compared to when contrast enhancement delay is not observed.

[0163] Figure 8A shows an example of an OCT image of a plaque (TCFA(+)CCs(+)) that was determined to satisfy both conditions (a) and (b). Figure 8B shows an example of an OCT image of a plaque (TCFA(+)CCs(-)) that was determined to satisfy condition (a) but not condition (b). Each OCT image was acquired using a commercially available imaging catheter (Dragonfly OpStar® Imaging Catheter, Abbott Laboratories).

[0164] The atherosclerotic plaques shown in the OCT images in Figure 8A had fibrous capsules with a thickness of 65 μm or less and contained coronary cysts (CCs). Specifically, the atherosclerotic plaque shown in the OCT image (1) on the left side of Figure 8A had a fibrous capsule with a thickness of approximately 60 μm, as indicated by the arrow. The OCT image (2) on the right side of Figure 8A was obtained by taking a cross-section 0.4 mm away in the longitudinal direction of the coronary artery from the cross-section of the coronary artery from which the OCT image (1) on the left side was obtained. In this OCT image (2), as indicated by the arrowheads, multiple CCs were visible within the atherosclerotic plaque, exhibiting higher intensity than the surrounding area, particularly strong posterior attenuation, and each having a linear shape extending in the circumferential direction of the coronary artery.

[0165] On the other hand, the atherosclerotic plaques shown in the OCT image in Figure 8B had a fibrous capsule less than 65 μm thick, but did not contain CCs. Specifically, the atherosclerotic plaque shown in the OCT image (1) on the left in Figure 8B had a fibrous capsule approximately 40 μm thick, as indicated by the arrow. Meanwhile, the OCT image (2) on the right in Figure 8B is an OCT image obtained by taking a cross-section 1.0 mm away in the longitudinal direction of the coronary artery from the cross-section of the coronary artery from which the OCT image (1) on the left was obtained. No CCs were found in this OCT image (2) either.

[0166] Figure 9 shows the evaluation results. In Figure 9, the horizontal axis shows the four judgment results, similar to Figure 7 of Example 2 described above, indicating whether conditions (a) and (b) are met or not, and the vertical axis shows the incidence of contrast-enhancing delay in catheter treatment (the ratio of the number of patients who experienced contrast-enhancing delay during catheter treatment to the total number of patients for each judgment result) (%).

[0167] As shown in Figure 9, the incidence of contrast-enhancing delay in catheter treatment (the ratio of patients who experienced contrast-enhancing delay during catheter treatment to the total number of patients (19)) in patients whose atherosclerotic ulcers were determined to meet both conditions (a) and (b) based on OCT imaging (TCFA(+)CCs(+)) was 42%, which was significantly higher than that of patients whose atherosclerotic ulcers were determined not to meet one or both conditions (a) and (b) (TCFA(-)CCs(-), TCFA(-)CCs(+), and TCFA(+)CCs(-)) (3% to 8%).

[0168] In other words, atherosclerotic plaques that were determined to satisfy both conditions (a) and (b) based on OCT imaging were at a higher risk of causing contrast-induced delays during catheter treatment compared to atherosclerotic plaques that were determined not to satisfy one or both of the conditions (a) and (b).

[0169] In Examples 2 and 3 described above, for 19 patients who were determined to have a fibrous capsule with a thickness of 65 μm or less and containing CCs (true TCFA) in their coronary arteries, the correlation between whether the true TCFA met further predetermined additional conditions and whether or not contrast enhancement delay was observed during catheter treatment was evaluated.

[0170] The following five additional conditions were used: (c1) the maximum number of CCs in the true TCFA is greater than or equal to a predetermined threshold; (c2) the maximum number of layers of CCs in the true TCFA in the radial direction of the coronary artery is greater than or equal to a predetermined threshold; (c3) the shortest distance between the CCs in the true TCFA and the inner surface of the coronary artery is less than or equal to a predetermined threshold; (d1) the maximum distribution angle of the true TCFA in the cross-section of the coronary artery is greater than or equal to a predetermined threshold; and (d2) the length of the true TCFA in the longitudinal direction of the coronary artery is greater than or equal to a predetermined threshold.

[0171] [Number of Cholesterol Crystals] Figure 10A shows an example of an OCT image from a patient in which the maximum number of CCs in true TCFA in a single OCT image was 1. Figure 10B shows an example of an OCT image from a patient in which the maximum number of CCs in true TCFA in a single OCT image was 5 or more.

[0172] In the OCT image shown in Figure 10A, only one CCs was observed within the true TCFA, as indicated by the arrowhead. On the other hand, in the OCT image shown in Figure 10B, at least six CCs were observed within the true TCFA, as indicated by the arrowhead.

[0173] Figure 11 shows the results of evaluating the correlation between the maximum number of CCs in the true TCFA in a single OCT image and the incidence of contrast-enhancing delay in catheter treatment (%). The maximum number of CCs was obtained by counting the number of CCs in the true TCFA in each of the multiple OCT images acquired for each patient, and the maximum number of CCs in a single OCT image was used.

[0174] As shown in Figure 11, among the eight patients in whom the maximum number of CCs in true TCFA was 1, only one patient experienced contrast enhancement delay during catheter treatment, resulting in a contrast enhancement delay rate of approximately 13%.

[0175] On the other hand, among the 11 patients (9+2) who were determined to meet the above condition (c1), which states that the maximum number of CCs in true TCFA is 2 or more, 7 patients (5+2) experienced contrast-enhancing delay during catheter treatment, resulting in an incidence of contrast-enhancing delay of approximately 64%.

[0176] Furthermore, of the two patients who were determined to meet the above condition (c1), which states that the maximum number of CCs in true TCFA is 5 or more, two patients experienced contrast enhancement delay during catheter treatment, resulting in a contrast enhancement delay incidence rate of 100%.

[0177] Thus, it was confirmed that the greater the maximum number of CCs included in the true TCFA in the OCT image, the higher the risk of contrast-enhancing delay during catheter treatment.

[0178] [Number of Cholesterol Crystal Layers] Figure 12A shows an example of an OCT image of true TCFA where the maximum number of CCs layers in the radial direction of the coronary artery was 1. Figure 12B shows an example of an OCT image of true TCFA where the maximum number of CCs layers in the radial direction of the coronary artery was 3.

[0179] The true TCFA shown in the OCT image in Figure 12A contained CCs with one layer in the radial direction of the coronary artery, as indicated by the arrowheads. On the other hand, the true TCFA shown in the OCT image in Figure 12B contained three CCs that formed three layers in the radial direction of the coronary artery, as indicated by the arrowheads.

[0180] Figure 13 shows the results of evaluating the correlation between the maximum number of layers of CCs in the radial direction of the coronary artery contained in the true TCFA as seen in OCT images and the incidence of contrast-enhancing delay (%) in catheter treatment. The maximum number of CC layers was obtained by counting the number of CC layers in the true TCFA in each of the multiple OCT images acquired for each patient.

[0181] As shown in Figure 13, among the seven patients with a maximum number of CCs layers of 1, none experienced contrast-enhancing delay during catheter treatment, and the incidence of contrast-enhancing delay was 0%.

[0182] On the other hand, among the 12 patients (=8+4) who were determined to meet the above condition (c2), which states that the maximum number of CCs layers in true TCFA is two or more, 8 patients (=4+4) experienced contrast-enhancing delay during catheter treatment, resulting in an incidence of contrast-enhancing delay of approximately 67%.

[0183] Furthermore, among the four patients who were determined to meet the above condition (c2), which states that the maximum number of CCs layers in true TCFA is three or more, four patients experienced contrast enhancement delay during catheter treatment, resulting in a contrast enhancement delay incidence rate of 100%.

[0184] Thus, it was confirmed that the greater the maximum number of CCs layers contained in true TCFA in OCT images, the higher the risk of contrast-enhancing delay during catheter treatment.

[0185] [Distance between cholesterol crystals and the inner surface of blood vessels] Figure 14A shows an example of an OCT image in which the shortest distance between CCs contained in true TCFA and the inner surface of the coronary artery was 90 μm or less. Figure 14B shows an example of an OCT image in which the shortest distance between CCs contained in true TCFA and the inner surface of the coronary artery was 151 μm or more.

[0186] In the true TCFA shown in the OCT image in Figure 14A, the distance between the CCs closest to the inner surface of the coronary artery (indicated by arrowheads) and the inner surface of the coronary artery was approximately 60 μm. On the other hand, in the true TCFA shown in the OCT image in Figure 14B, the distance between the CCs closest to the inner surface of the coronary artery (indicated by arrowheads) and the inner surface of the coronary artery was approximately 270 μm.

[0187] Figure 15 shows the results of evaluating the correlation between the shortest distance (μm) between CCs contained in the true TFCA visible in the OCT image and the inner surface of the coronary artery, and the incidence of contrast-enhancing delay (%) in catheter treatment.

[0188] As shown in Figure 15, among the four patients in whom the shortest distance between CCs in true TCFA and the inner surface of the coronary artery was 151 μm or more, none of them experienced contrast enhancement delay during catheter treatment, and the incidence of contrast enhancement delay was 0%.

[0189] On the other hand, among the 14 patients (7+7) who were determined to meet the above condition (c3), which states that the shortest distance between CCs included in true TCFA and the inner surface of the coronary artery is 150 μm or less, 8 patients (5+3) experienced delayed contrast enhancement during catheter treatment, resulting in an incidence of delayed contrast enhancement of approximately 57%.

[0190] Furthermore, among the seven patients who were determined to meet the above condition (c3), which states that the shortest distance between CCs included in true TCFA and the inner surface of the coronary artery is 90 μm or less, five patients experienced delayed contrast enhancement during catheter treatment, resulting in an incidence of delayed contrast enhancement of approximately 71%.

[0191] Thus, it was confirmed that the smaller the shortest distance between CCs included in the true TCFA and the inner surface of the coronary artery in the OCT image, the higher the risk of contrast-enhancing delay during catheter treatment.

[0192] [Distribution angle of atherosclerotic plaque] Figure 16A shows an example of an OCT image in which the true TCFA distribution angle in the cross-section of the coronary artery was less than 180°. Figure 16B shows an example of an OCT image in which the true TCFA distribution angle in the cross-section of the coronary artery was 270° or greater.

[0193] The distribution angle of true TCFA shown in the OCT image in Figure 16A was approximately 60°. On the other hand, true TCFA shown in the OCT image in Figure 16B was formed throughout the entire circumferential region of the coronary artery, and its distribution angle was 360°.

[0194] Figure 17 shows the results of evaluating the correlation between the maximum value (°) of the true TCFA distribution angle in a single OCT image and the incidence of contrast-enhancing delay (%) in catheter treatment. The maximum value of the true TCFA distribution angle was obtained by identifying the distribution angle within the true TCFA in each of the multiple OCT images acquired for each patient, and the maximum value of that distribution angle was used.

[0195] As shown in Figure 17, among the six patients whose true TCFA distribution angle in the coronary artery cross-section was less than 180°, only one patient experienced contrast enhancement delay during catheter treatment, resulting in an incidence of contrast enhancement delay of approximately 17%.

[0196] On the other hand, among the 13 patients (7+6) who were determined to meet the above condition (d1), which is that the true TCFA distribution angle in the cross-section of the coronary artery is 180° or more, 7 patients (3+4) experienced delayed contrast enhancement during catheter treatment, resulting in an incidence of delayed contrast enhancement of approximately 54%.

[0197] Furthermore, among the six patients who were determined to meet the above condition (d1), which is that the true TCFA distribution angle in the cross-section of the coronary artery is 270° or greater, four patients experienced contrast enhancement delay during catheter treatment, resulting in an incidence of contrast enhancement delay of approximately 67%.

[0198] Thus, it was confirmed that the larger the distribution angle of true TCFA in the cross-section of the coronary artery in the OCT image, the higher the risk of contrast-enhancing delay during catheter treatment.

[0199] [Length of atherosclerotic plaque] Figure 18 shows the results of evaluating the correlation between the longitudinal length (mm) of the true TCFA coronary artery as seen in OCT images and the incidence of contrast-enhancing delay (%) in catheter treatment.

[0200] As shown in Figure 18, among the six patients in whom the true TCFA length in the longitudinal direction of the coronary artery was less than 5 mm, only one patient experienced delayed contrast enhancement during catheter treatment, resulting in an incidence of delayed contrast enhancement of approximately 17%.

[0201] On the other hand, among the 13 patients (=8+5) who were determined to meet the above condition (d2), which is that the true TCFA length in the longitudinal direction of the coronary artery is 5 mm or more, 7 patients (=3+4) experienced delayed contrast enhancement during catheter treatment, resulting in an incidence of delayed contrast enhancement of approximately 54%.

[0202] Furthermore, among the five patients who were determined to meet the above condition (d2), which states that the true TCFA length in the longitudinal direction of the coronary artery is 10 mm or more, four patients experienced delayed contrast enhancement during catheter treatment, resulting in an incidence of delayed contrast enhancement of 80%.

[0203] Thus, it was confirmed that the greater the true TCFA length in the longitudinal direction of the coronary artery in OCT images, the higher the risk of contrast enhancement delay during catheter treatment.

Claims

1. A method for determining the risk of plaque, comprising: a condition determination step of determining whether a plaque visible in one or more tomographic images of a patient's blood vessels acquired by optical interference imaging satisfies the following conditions: (a) the plaque has a fibrous capsule whose thickness is less than or equal to a predetermined threshold; and (b) the plaque contains cholesterol crystals; and a risk determination step of determining, if the plaque is determined to satisfy conditions (a) and (b) in the condition determination step, that a plaque satisfying conditions (a) and (b) is a higher-risk plaque than a plaque that does not satisfy one or both of conditions (a) and (b).

2. The method according to claim 1, wherein, in the condition determination step, it is determined that the atherosclerotic plaque satisfies conditions (a) and (b), and in the risk determination step, it is determined that the atherosclerotic plaque that satisfies conditions (a) and (b) has a higher risk of causing vascular disease or complications during treatment compared to atherosclerotic plaque that does not satisfy one or both of conditions (a) and (b).

3. The method according to claim 1 or 2, further comprising: an additional condition determination step, in which it is determined in the condition determination step that the atherosclerosis satisfies conditions (a) and (b), and it is determined whether the atherosclerosis that satisfies conditions (a) and (b) further satisfies one or more additional conditions selected from the group consisting of: (c1) the representative value of the number of cholesterol crystals is greater than or equal to a predetermined threshold; (c2) the representative value of the number of layers of cholesterol crystals in the radial direction of the blood vessel is greater than or equal to a predetermined threshold; and (c3) the shortest distance between the cholesterol crystals and the inner surface of the blood vessel is less than or equal to a predetermined threshold; and an additional risk determination step, in which it is determined in the additional condition determination step that the atherosclerosis satisfies one or more of the additional conditions, and it is determined that a specific atherosclerosis that satisfies conditions (a) and (b) and one or more of the additional conditions is a higher-risk atherosclerosis than an atherosclerosis that satisfies conditions (a) and (b) and does not satisfy the additional conditions that the specific atherosclerosis satisfies.

4. The method according to claim 1 or 2, further comprising: an additional condition determination step, which determines whether, if it is determined in the condition determination step that conditions (a) and (b) are met, the atherosclerotic plaque that meets conditions (a) and (b) further satisfies one or more additional conditions selected from the group consisting of: (d1) the representative value of the distribution angle of the atherosclerotic plaque in the cross-section of the blood vessel is greater than or equal to a predetermined threshold; and (d2) the length of the atherosclerotic plaque in the longitudinal direction of the blood vessel is greater than or equal to a predetermined threshold; and an additional risk determination step, which determines, if it is determined in the additional condition determination step that the atherosclerotic plaque satisfies one or more of the additional conditions, that a specific atherosclerotic plaque that satisfies conditions (a) and (b) and one or more of the additional conditions is a higher-risk atherosclerotic plaque than an atherosclerotic plaque that satisfies conditions (a) and (b) and does not satisfy the additional conditions that the specific atherosclerotic plaque satisfies.

5. A risk determination device for atherosclerosis, comprising: a condition determination unit that determines whether atherosclerosis visible in one or more tomographic images of a patient's blood vessels obtained by optical interference imaging satisfies the following conditions: (a) the atherosclerosis has a fibrous capsule whose thickness is less than or equal to a predetermined threshold; and (b) the atherosclerosis contains cholesterol crystals; and a risk determination unit that, when the condition determination unit determines that the atherosclerosis satisfies conditions (a) and (b), generates determination result data indicating that the atherosclerosis satisfying conditions (a) and (b) is a higher-risk atherosclerosis compared to atherosclerosis that does not satisfy one or both of conditions (a) and (b).

6. The apparatus according to claim 5, wherein, when the condition determination unit determines that the atherosclerosis satisfies conditions (a) and (b), the risk determination unit generates determination result data indicating that the atherosclerosis satisfying conditions (a) and (b) is a theatherosclerosis with a higher risk of causing vascular disease or complications during treatment compared to atherosclerosis that does not satisfy one or both of conditions (a) and (b).

7. When the condition determination unit determines that the plaque satisfies conditions (a) and (b), the condition determination unit determines whether the plaque, which has been determined to satisfy conditions (a) and (b), further satisfies one or more additional conditions selected from the group consisting of: (c1) the representative value of the number of cholesterol crystals is greater than or equal to a predetermined threshold; (c2) the representative value of the number of layers of cholesterol crystals in the radial direction of the blood vessel is greater than or equal to a predetermined threshold; and (c3) the shortest distance between the cholesterol crystals and the inner surface of the blood vessel is less than or equal to a predetermined threshold; and when the condition determination unit determines that the plaque satisfies one or more of the additional conditions, the risk determination unit generates determination result data indicating that the specific plaque, which satisfies conditions (a) and (b) and one or more of the additional conditions, is a higher-risk plaque than plaque that satisfies conditions (a) and (b) but does not satisfy the additional conditions that the specific plaque satisfies. The apparatus according to claim 5 or 6.

8. The apparatus according to claim 5 or 6, wherein, if the condition determination unit determines that the atherosclerosis satisfies conditions (a) and (b), the determination condition unit determines whether the atherosclerosis determined to satisfy conditions (a) and (b) further satisfies one or more additional conditions selected from the group consisting of: (d1) the representative value of the distribution angle of the atherosclerosis in the cross-section of the blood vessel is greater than or equal to a predetermined threshold; and (d2) the length of the atherosclerosis in the longitudinal direction of the blood vessel is greater than or equal to a predetermined threshold; and if the condition determination unit determines that the atherosclerosis satisfies one or more of the additional conditions, the risk determination unit generates determination result data indicating that a specific atherosclerosis that satisfies conditions (a) and (b) and one or more of the additional conditions is a higher-risk atherosclerosis than an atherosclerosis that satisfies conditions (a) and (b) and does not satisfy the additional conditions that the specific atherosclerosis satisfies.

9. A risk determination program for atherosclerosis, which causes a computer to perform the following steps: a condition determination step of determining whether atherosclerosis visible in one or more tomographic images of a patient's blood vessels acquired by optical interference imaging satisfies the following conditions: (a) the atherosclerosis has a fibrous capsule whose thickness is less than or equal to a predetermined threshold; and (b) the atherosclerosis contains cholesterol crystals; and a risk determination step of generating determination result data indicating that, if the condition determination step determines that the atherosclerosis satisfies conditions (a) and (b), the atherosclerosis that satisfies conditions (a) and (b) is a higher-risk atherosclerosis than atherosclerosis that does not satisfy one or both of conditions (a) and (b).

10. The program according to claim 9, wherein the computer is instructed in the condition determination step to perform a risk determination step that generates determination result data indicating that the atherosclerotic plaque that satisfies conditions (a) and (b) is at a higher risk of causing vascular disease or complications during treatment compared to atherosclerotic plaque that does not satisfy one or both of conditions (a) and (b).

11. The computer is further instructed to perform an additional condition determination step, in which, if it is determined in the condition determination step that the atherosclerosis satisfies conditions (a) and (b), it determines whether the atherosclerosis that satisfies conditions (a) and (b) also satisfies one or more additional conditions selected from the group consisting of: (c1) the representative value of the number of cholesterol crystals is greater than or equal to a predetermined threshold; (c2) the representative value of the number of layers of cholesterol crystals in the radial direction of the blood vessel is greater than or equal to a predetermined threshold; and (c3) the shortest distance between the cholesterol crystals and the inner surface of the blood vessel is less than or equal to a predetermined threshold; and if it is determined in the additional condition determination step that the atherosclerosis satisfies one or more of the additional conditions, it further instructs the computer to perform an additional risk determination step, which generates determination result data indicating that a specific atherosclerosis that satisfies conditions (a) and (b) and one or more of the additional conditions is a higher-risk atherosclerosis than an atherosclerosis that satisfies conditions (a) and (b) and does not satisfy the additional conditions that the specific atherosclerosis satisfies. The program according to claim 9 or 10.

12. The program according to claim 9 or 10, which causes a computer to further perform: an additional condition determination step in which, if the condition determination step determines that the atherosclerosis satisfies conditions (a) and (b), the atherosclerosis that satisfies conditions (a) and (b) further satisfies one or more additional conditions selected from the group consisting of: (d1) the representative value of the distribution angle of the atherosclerosis in the cross-section of the blood vessel is greater than or equal to a predetermined threshold; and (d2) the length of the atherosclerosis in the longitudinal direction of the blood vessel is greater than or equal to a predetermined threshold; and an additional risk determination step in which, if the additional condition determination step determines that the atherosclerosis satisfies one or more of the additional conditions, the program generates determination result data indicating that a specific atherosclerosis that satisfies conditions (a) and (b) and one or more of the additional conditions is a higher-risk atherosclerosis than an atherosclerosis that satisfies conditions (a) and (b) and does not satisfy the additional conditions that the specific atherosclerosis satisfies.

Images